Manager, Data Operations & Analytics

The Manager – Data Operations & Analytics will lead the University’s data operations and analytics function, working with stakeholders across academic, corporate and ITS functions to deliver reliable, scalable and data-driven solutions aligned with institutional goals.

Key Responsibilities

• Lead the daily operations of the enterprise data lake, data warehouse and data marts, ensuring reliable data ingestion, processing and monitoring.

• Set the architecture, standards and best practices for scalable ETL/ELT pipelines integrating data from multiple sources.

• Engage stakeholders to gather and prioritise business requirements and translate them into effective technical solutions.

• Establish and enforce standards for data quality, validation, cleansing and transformation.

• Guide the development and implementation of dashboards, reports and advanced analytics solutions that support strategic and operational decision-making.

• Oversee the development, validation and performance monitoring of predictive and prescriptive models.

• Ensure data reliability, security and compliance with internal governance requirements in collaboration with ITS Cybersecurity.

• Lead cross-functional data and analytics projects, including planning, budgeting, vendor coordination and reporting to senior stakeholders.

• Promote the use of machine learning, segmentation, optimisation and visualisation to generate actionable insights.

• Set technical standards for coding, version control, CI/CD and reproducible development practices.

• Lead, mentor and develop the data team, ensuring effective delivery and continuous capability development.

• Serve as the analytics subject matter expert, advising stakeholders and leadership on the design and interpretation of reports, dashboards and analytical outputs.

Job Requirements

• Bachelor’s degree in Computer Science, Data Science, Information Technology or a related field. A master’s degree is an advantage.

• Minimum 10 years of overall work experience, including at least five years in a lead or managerial capacity within data engineering, analytics or data operations.

• Proven experience managing enterprise data platforms, data pipelines and large-scale analytics projects.

• Experience in project planning, budgeting, vendor management and reporting to senior stakeholders.

• Experience contributing to data governance policies or organisational data strategy.

• Strong leadership and mentoring skills, with experience developing high-performing teams.

• Excellent stakeholder management and communication skills, with the ability to translate technical concepts into business value.

• Strategic, organised and results-oriented, with strong analytical and problem-solving capabilities.

• Proactive, adaptable and committed to continuous learning and promoting a data-driven culture.

Technical Competencies

• Strong knowledge of data architecture, ETL/ELT pipelines and enterprise data platforms, including data lakes, data warehouses and data marts.

• Proficiency in SQL, Python and cloud platforms such as AWS, Azure or GCP.

• Experience with cloud-native data services such as AWS Glue, Athena, S3 and Redshift.

• Familiarity with distributed computing technologies such as Apache Spark or Hadoop.

• Experience in machine learning, advanced analytics, statistical modelling or artificial intelligence.

• Ability to guide and evaluate predictive and prescriptive models, including model validation and performance monitoring.

• Knowledge of data governance, metadata management, data quality, data security and privacy requirements.

• Familiarity with version control, CI/CD practices and collaborative development tools such as Git and JIRA.

• Ability to assess vendor solutions and integrate third-party platforms into enterprise data workflows.